A major goal in biomedicine is the identification of disease biomarkers in biological fluids that can be used in disease screening, early diagnosis, and monitoring of therapeutic approaches. The development of such biomarkers for lysosomal storage diseases (LSDs) has not been successful thus far. However, the availability of these biomarkers is needed and absolutely necessary to evaluate the safety and efficacy of disease interventions, especially for those LSDs with a neurodegenerative phenotype. Most biomarker studies have focused on comparing plasma proteins between a normal and a disease state. The major problem with this approach is the complexity of the plasma proteome, i.e. the proteins that can be found circulating in plasma. We have established a novel approach for identifying disease biomarkers in plasma and other fluids such as urine. This unique approach eliminates the difficulties inherent in analysis of the plasma proteome by first identifying potential biomarker proteins in a less complex system: the exosome. Exosomes are uniquely suited for the identification of the LSD disease biomarkers. They are small vesicles derived from the membranes of late endosomes that contain a subset of proteins normally found in endosomes. Many cells secrete exosome and thus it is likely that the proteins contained within them can be isolated and identified from cellular secretions. Because LSDs often present with abnormalities in endosomal/lysosomal compartments, we hypothesize that exosome will reflect protein and lipid changes specific to the disease defect exhibited by the cell from which they are derived/isolated. Here, we propose to: 1) test the prediction that exosomes from Neimann-Pick C1 cells will have unique protein and/or lipid identifiers (biomarkers) that will distinguish them from those of normal cells; and 2) evaluate whether these potential biomarkers are detectable in biological fluids including plasma, urine and CSF; and 3) test the prediction that changes in glucose metabolism correlate with NPC1 disease severity and can be used to monitor disease progression. In short, this new approach could facilitate the efficient discovery of LSD biomarkers and provide us with the next step in developing therapies that can be evaluated a in meaningful way in clinical trials.